Untargeted urine metabolite profiling by mass spectrometry aided by multivariate statistical analysis to predict prostate cancer treatment outcome

Yiwei Ma, Zhaoyu Zheng, Sihang Xu, Athula Attygalle, Isaac Yi Kim, Henry Du

Research output: Contribution to journalArticlepeer-review

2 Scopus citations

Abstract

Deciphering metabolomic networks has been demonstrated to provide valuable information for diagnosing and monitoring diseases. Herein, we report a technique to monitor untargeted urine metabolites to evaluate prostate cancer aggressiveness and treatment outcome. Direct chemical profiling of urine was achieved by a combined procedure of hyphenating laser diode thermal desorption with atmospheric pressure chemical ionization mass spectrometry (LDTD-APCI-MS). We describe a conceptually new approach to monitoring preoperative urinary metabolic alterations associated with prostate cancer recurrence. By evaluating mass/charge (m/z) ratios and peak intensities of ions detected by mass spectroscopy of urine samples, we revealed that intensities at m/z 313.2740 (±0.0003) and 341.3054 (±0.0006) attributable to monoacylglycerol backbone fragments from glycerides can be statistically correlated to disease progression.

Original languageEnglish
Pages (from-to)3043-3054
Number of pages12
JournalAnalyst
Volume147
Issue number13
DOIs
StatePublished - 26 May 2022

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